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The current methods to perform task-agnostic or task-specific semantic rule mining operate, however, a completely different KG representation, making them less suitable to perform both tasks or incorporate each other\u2019s optimizations. This also results in the need to master multiple techniques for both exploring and mining rules within KGs, as well losing time and resources when converting one KG format into another. In this paper, we use INK, a KG representation based on neighbourhood nodes of interest to mine rules for improved decision support. By selecting one or two sets of nodes of interest, the rule miner created on top of the INK representation will either mine task-agnostic or task-specific rules. In both subfields, the INK miner is competitive to the currently state-of-the-art semantic rule miners on 14 different benchmark datasets within multiple domains.<\/jats:p>","DOI":"10.3233\/sw-233495","type":"journal-article","created":{"date-parts":[[2023,10,3]],"date-time":"2023-10-03T11:45:29Z","timestamp":1696333529000},"page":"1367-1388","source":"Crossref","is-referenced-by-count":1,"title":["INK: Knowledge graph representation for efficient and performant rule mining"],"prefix":"10.1177","volume":"15","author":[{"given":"Bram","family":"Steenwinckel","sequence":"first","affiliation":[{"name":"Internet and Data Lab, Ghent University, Technologiepark-zwijnaarde 126, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Filip","family":"De Turck","sequence":"additional","affiliation":[{"name":"Internet and Data Lab, Ghent University, Technologiepark-zwijnaarde 126, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Femke","family":"Ongenae","sequence":"additional","affiliation":[{"name":"Internet and Data Lab, Ghent University, Technologiepark-zwijnaarde 126, Gent, Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/SW-233495_ref1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-76298-0_52"},{"key":"10.3233\/SW-233495_ref2","doi-asserted-by":"publisher","DOI":"10.3390\/s18113832"},{"key":"10.3233\/SW-233495_ref3","doi-asserted-by":"publisher","DOI":"10.1145\/2851613.2851842"},{"key":"10.3233\/SW-233495_ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03667-6_7"},{"key":"10.3233\/SW-233495_ref6","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.websem.2013.01.002","article-title":"Binary RDF representation for publication and exchange (HDT)","volume":"19","author":"Fern\u00e1ndez","year":"2013","journal-title":"Web Semantics: Science, Services and Agents on the World Wide Web"},{"key":"10.3233\/SW-233495_ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3018661.3018739"},{"issue":"6","key":"10.3233\/SW-233495_ref8","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1007\/s00778-015-0394-1","article-title":"Fast rule mining in ontological knowledge bases with AMIE+","volume":"24","author":"Gal\u00e1rraga","year":"2015","journal-title":"The VLDB Journal"},{"key":"10.3233\/SW-233495_ref9","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3511925"},{"key":"10.3233\/SW-233495_ref10","doi-asserted-by":"crossref","unstructured":"V.T.\u00a0Ho, D.\u00a0Stepanova, M.H.\u00a0Gad-Elrab, E.\u00a0Kharlamov and G.\u00a0Weikum, Rule learning from knowledge graphs guided by embedding models, in: International Semantic Web Conference, Springer, 2018, pp.\u00a072\u201390.","DOI":"10.1007\/978-3-030-00671-6_5"},{"key":"10.3233\/SW-233495_ref11","doi-asserted-by":"crossref","unstructured":"A.\u00a0Hogan, E.\u00a0Blomqvist, M.\u00a0Cochez, C.\u00a0d\u2019Amato and G.\u00a0d\u00a0Melo, Knowledge graphs. 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